A Simple Clinically Based Predictive Rule for Heart Failure In-Hospital Mortality
Received 4 April 2006; received in revised form 29 May 2006; accepted 20 June 2006.
Abstract
Background
Scarce data are available to predict in-hospital mortality for decompensated heart failure (HF) in South American populations.
Methods and Results
We evaluated 779 consecutive HF admissions defined by the Boston criteria in a tertiary care hospital. Stepwise logistic regression was used to determine independent correlates of in-hospital mortality, derived from 83 potential predictors collected on hospital admission. A clinical score rule (HF Revised Score) was created using the regression coefficient estimates derived from multivariate modeling. During hospital stay, 77 (10%) deaths occurred and 6 clinical characteristics were independently associated with in-hospital mortality: presence of cancer (P < .001), systolic blood pressure ≤124 mm Hg (P < .001), serum creatinine >1.4 mg/dL (P = .02), blood urea nitrogen >37 mg/dL (P = .03), serum sodium <136 mEq/L (P = .03), and age >70 years old (P = .03). Both the Acute Decompensated Heart Failure National Registry stratification algorithm and the proposed HF Revised Score performed adequately to predict in-hospital mortality (“c” statistics = 0.71 and 0.76, respectively). The newly proposed score, however, discriminated a very low-risk group (101 [13%]) in whom all patients were discharged home, representing patients admitted with none of the 6 predictors of risk.
Conclusion
HF risk stratification can be accurately accomplished during the first day of admission with simple and easily obtained clinical variables.
From the Heart Failure and Cardiac Transplantation Unit, Cardiology Division at Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul Medical School, Porto Alegre, Brazil
Reprint requests: Luis E. Rohde, MD, ScD, Cardiology Division, Room 2061, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, RS, Brazil 90035–903.
Supported in part by grants from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Rio Grande do Sul (FAPERGS) and Fundo de Incentivo à Pesquisa (FIPE-HCPA).
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